Agilent Single-Channel Microarray Processing Pipeline RMarkdown (GLDS-174)

Installing necessary packages

For processing Agilent microarray data, I needed to install a few packages that were used for the importing and analyzing the data. This included Bioconductor as well as Linear Models for Microarray (Limma). These are popular packages for processing microarray data.

## For installing Bioconductor on R
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install(version = "3.14")

## For installing limma on R
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("limma")

Load the packages

After installing the packages above, I had to load them in using the library function so that I could access their functions.

library(limma)

Importing the Raw Data

Now that the library was loaded, I was able to import the raw data. I started by importing the runsheet into R. This contained the necessary data for processing later in the pipeline.

dir = "C:/Users/tangk/Desktop/Agilent_Microarray"
GLDS_174_rs <- read.csv(file.path(dir, "Runsheet(GLDS-174).csv"), check.names = FALSE, fileEncoding = 'UTF-8-BOM') ## Outputs a dataframe
Factor_Value <- GLDS_174_rs[,c("Factor Value[sex]", "Factor Value[exposure duration]")]

dir = A variable that stores the location of where I kept the runsheets.
GLDS_174_rs = A varaible that holds the contents of those runsheets.
Factor_Value = A variable that holds specifically the factor values column from the runsheet.

Here, I imported the raw data from the text files into R using the readmaimages() function from the Limma package.

datadir = "C:/Users/tangk/Desktop/Agilent_Microarray/input_data"
files = dir(path = file.path(datadir, "GLDS-174/GLDS-174_Raw_Data_Combined"), pattern="*\\.txt$")

## Input: ".txt" files, Output: EListRaw (Raw expression levels)
raw_data <- limma::read.maimages(files, source="agilent", path = file.path(datadir, "GLDS-174/GLDS-174_Raw_Data_Combined"), sep="\t", green.only = TRUE)

## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930654_1p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930655_1p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930656_1p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930657_1p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930658_1p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930659_1p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930660_3p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930661_3p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930662_3p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930663_3p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930664_3p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930665_3p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930666_4p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930667_4p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930668_4p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930669_4p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930670_4p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930671_4p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930672_6p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930673_6p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930674_6p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930675_6p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930676_6p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930677_6p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930678_7p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930679_7p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930680_7p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930681_7p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930682_7p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930683_7p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930684_8p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930685_8p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930686_8p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930687_8p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930688_8p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930689_8p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930690_9p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930691_9p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930692_9p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930693_9p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930694_9p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930695_9p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930696_10p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930697_10p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930698_10p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930699_10p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930700_10p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930701_10p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930702_11p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930703_11p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930704_11p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930705_11p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930706_11p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930707_11p6.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930708_12p1.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930709_12p2.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930710_12p3.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930711_12p4.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930712_12p5.txt 
## Read C:/Users/tangk/Desktop/Agilent_Microarray/input_data/GLDS-174/GLDS-174_Raw_Data_Combined/GSM1930713_12p6.txt

datadir = A variable that stores the path to the directory that holds the three datasets that I have chosen.
files = A variable that will detect all of the text files within the GLDS-174_Raw_Data_Combined directory. This is where all of the raw Agilent microarray data is stored for the GLDS-174 dataset.
source = “agilent” because the data that I want to import will be agilent microarray data. There are other options for this flag for other types of microarrays
path = The path to the directory that holds the dataset’s raw data.
sep = How the data is separated in those raw data files (in our case it’s tab separated).
green.only = TRUE, This command is for telling the function that it should only read the green (Cys3) channel rather than red and green since we’re only looking at single channel microarrays.

QC of the Raw Data

In this step, I looked at the raw data to make sure that it is in the correct format so that it could be processed correctly down the pipeline.

# This code block uses the plotDensities() function from limma to generate a graph with the raw intensities vs. density 
plotDensities(raw_data, log = TRUE, legend = "topright" , main = "Density of raw expression values for multiple arrays")

## Warning in plotDensities.EListRaw(raw_data, log = TRUE, legend = "topright", :
## NaNs produced

raw_data = EListRaw object containing the raw expression values and information about them
log = Specifying the densities should be plotted on the log2 scale
legend = Specifying where the legend should be placed on the graph
main = title of the graph

# This code block uses the imageplot() function to generate a pseudoimage for each sample to allow the user to look for any spatial abnormalities in the expressions on the chip.
for (sample_name in colnames(raw_data$E)) {
    print(sample_name)
    imageplot(raw_data$E[,sample_name], layout=list(ngrid.r = 1, ngrid.c = 1, nspot.r = 809 , nspot.c =55), zlim = c((1.25*min(raw_data$E[,sample_name])),(0.75*max(raw_data$E[,sample_name]))), legend = TRUE)
} # The $printer variable didn't exist for this sample as well. I just searched up factors for 44495 (# of rows) and entered in one of them and it worked.

## [1] "GSM1930654_1p1"

## [1] "GSM1930655_1p2"

## [1] "GSM1930656_1p3"

## [1] "GSM1930657_1p4"

## [1] "GSM1930658_1p5"

## [1] "GSM1930659_1p6"

## [1] "GSM1930660_3p1"

## [1] "GSM1930661_3p2"

## [1] "GSM1930662_3p3"

## [1] "GSM1930663_3p4"

## [1] "GSM1930664_3p5"

## [1] "GSM1930665_3p6"

## [1] "GSM1930666_4p1"

## [1] "GSM1930667_4p2"

## [1] "GSM1930668_4p3"

## [1] "GSM1930669_4p4"

## [1] "GSM1930670_4p5"

## [1] "GSM1930671_4p6"

## [1] "GSM1930672_6p1"

## [1] "GSM1930673_6p2"

## [1] "GSM1930674_6p3"

## [1] "GSM1930675_6p4"

## [1] "GSM1930676_6p5"

## [1] "GSM1930677_6p6"

## [1] "GSM1930678_7p1"

## [1] "GSM1930679_7p2"

## [1] "GSM1930680_7p3"

## [1] "GSM1930681_7p4"

## [1] "GSM1930682_7p5"

## [1] "GSM1930683_7p6"

## [1] "GSM1930684_8p1"

## [1] "GSM1930685_8p2"

## [1] "GSM1930686_8p3"

## [1] "GSM1930687_8p4"

## [1] "GSM1930688_8p5"

## [1] "GSM1930689_8p6"

## [1] "GSM1930690_9p1"

## [1] "GSM1930691_9p2"

## [1] "GSM1930692_9p3"

## [1] "GSM1930693_9p4"

## [1] "GSM1930694_9p5"

## [1] "GSM1930695_9p6"

## [1] "GSM1930696_10p1"

## [1] "GSM1930697_10p2"

## [1] "GSM1930698_10p3"

## [1] "GSM1930699_10p4"

## [1] "GSM1930700_10p5"

## [1] "GSM1930701_10p6"

## [1] "GSM1930702_11p1"

## [1] "GSM1930703_11p2"

## [1] "GSM1930704_11p3"

## [1] "GSM1930705_11p4"

## [1] "GSM1930706_11p5"

## [1] "GSM1930707_11p6"

## [1] "GSM1930708_12p1"

## [1] "GSM1930709_12p2"

## [1] "GSM1930710_12p3"

## [1] "GSM1930711_12p4"

## [1] "GSM1930712_12p5"

## [1] "GSM1930713_12p6"

raw_data$E[, sample_name] = specifying that we want to use the raw expression values (rows) from each sample (column) as the data for a plot
layout = using the values from the printer variable from raw_data to specify the dimensions of the pseudoimage generated
zlim = altering the range of intensities that will be plotted (min = 1.25 min raw expression value, max = 0.75 max raw expression value)
legend = displays the range of the z values (raw expresison values)

# This code block uses the plotMA() function from limma to generate a MA plot for each of the samples. 
count = 0
for (sample_name in colnames(raw_data$E)) {
    count = count + 1
    limma::plotMA(raw_data,array=count,xlab="Average log-expression",ylab="Expression log-ratio(this sample vs. others)", main = sample_name)
}

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

## Warning in plotMA.EListRaw(raw_data, array = count, xlab = "Average log-
## expression", : NaNs produced

raw_data = EListRaw object containing the raw expression values and information about them
array = choosing which array (sample) to use the data from
xlab = label for the x axis
ylab = label for the y axis
main = title for the plot

# This code block uses the plotFB() function from limma to generate a FB plot to compare the foreground and background intensities in each sample. 
count = 0
for (sample_name in colnames(raw_data$E)) {
    count = count + 1
    plotFB(raw_data, array = count, xlab = "log2 Background", ylab = "log2 Foreground", main = sample_name) 
}

raw_data = EListRaw object containing the raw expression values and information about them
array = specifying which array (sample) to use as the data
xlab = label for the x axis
ylab = label for the y axis
main = title for the plot

Normalization of the Data

After making sure that the raw data was formatted properly, I moved on to removing a lot of the variation in the gene expression data that comes from external sources (i.e. Sample preparation, labeling, hybridization) by performing normalization on the raw data. For this step, I was able to use a couple other functions from Limma called backgroundCorrect() to subtract the foreground intensities from the background intensities and normalizeBetweenArrays() to normalize the data.

corrected_data <- limma::backgroundCorrect(raw_data, method = "normexp", verbose=TRUE)

## Array 1 corrected
## Array 2 corrected
## Array 3 corrected
## Array 4 corrected
## Array 5 corrected
## Array 6 corrected
## Array 7 corrected
## Array 8 corrected
## Array 9 corrected
## Array 10 corrected
## Array 11 corrected
## Array 12 corrected
## Array 13 corrected
## Array 14 corrected
## Array 15 corrected
## Array 16 corrected
## Array 17 corrected
## Array 18 corrected
## Array 19 corrected
## Array 20 corrected
## Array 21 corrected
## Array 22 corrected
## Array 23 corrected
## Array 24 corrected
## Array 25 corrected
## Array 26 corrected
## Array 27 corrected
## Array 28 corrected
## Array 29 corrected
## Array 30 corrected
## Array 31 corrected
## Array 32 corrected
## Array 33 corrected
## Array 34 corrected
## Array 35 corrected
## Array 36 corrected
## Array 37 corrected
## Array 38 corrected
## Array 39 corrected
## Array 40 corrected
## Array 41 corrected
## Array 42 corrected
## Array 43 corrected
## Array 44 corrected
## Array 45 corrected
## Array 46 corrected
## Array 47 corrected
## Array 48 corrected
## Array 49 corrected
## Array 50 corrected
## Array 51 corrected
## Array 52 corrected
## Array 53 corrected
## Array 54 corrected
## Array 55 corrected
## Array 56 corrected
## Array 57 corrected
## Array 58 corrected
## Array 59 corrected
## Array 60 corrected

# Input: Background corrected EListRaw, Output: Elist
norm_data <- normalizeBetweenArrays(corrected_data, method = "quantile")

QC of the Normalized Data

count = 0 
for (sample_name in colnames(raw_data$E)) {
    count = count + 1
    limma::plotMA(norm_data, array = count, xlab = "Average Log-expression", ylab = "Expression Log-ratio (this sample vs. others)", main = sample_name)
}